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IRIS
Abstract
OBJECTIVES:
Improvement of skin fibrosis is part of the natural course of diffuse cutaneous systemic sclerosis (dcSSc). Recognising those patients most likely to improve could help tailoring clinical management and cohort enrichment for clinical trials. In this study, we aimed to identify predictors for improvement of skin fibrosis in patients with dcSSc.
METHODS:
We performed a longitudinal analysis of the European Scleroderma Trials And Research (EUSTAR) registry including patients with dcSSc, fulfilling American College of Rheumatology criteria, baseline modified Rodnan skin score (mRSS) ≥7 and follow-up mRSS at 12±2 months. The primary outcome was skin improvement (decrease in mRSS of >5 points and ≥25%) at 1 year follow-up. A respective increase in mRSS was considered progression. Candidate predictors for skin improvement were selected by expert opinion and logistic regression with bootstrap validation was applied.
RESULTS:
From the 919 patients included, 218 (24%) improved and 95 (10%) progressed. Eleven candidate predictors for skin improvement were analysed. The final model identified high baseline mRSS and absence of tendon friction rubs as independent predictors of skin improvement. The baseline mRSS was the strongest predictor of skin improvement, independent of disease duration. An upper threshold between 18 and 25 performed best in enriching for progressors over regressors.
CONCLUSIONS:
Patients with advanced skin fibrosis at baseline and absence of tendon friction rubs are more likely to regress in the next year than patients with milder skin fibrosis. These evidence-based data can be implemented in clinical trial design to minimise the inclusion of patients who would regress under standard of care.
Prediction of improvement in skin fibrosis in diffuse cutaneous systemic sclerosis: A EUSTAR analysis
Dobrota, Rucsandra;Maurer, Britta;Graf, Nicole;Jordan, Suzana;Mihai, Carina;Kowal Bielecka;Otylia, Allanore;Yannick, Distler;Oliver;Marco Matucci Cerinic;GUIDUCCI, SERENA;Ulrich Walker;Giovanni Lapadula;Florenzo Iannone;Radim Becvar;Stanislaw Sierakowsky;Maurizio Cutolo;Alberto Sulli;Gabriele Valentini;Giovanna Cuomo;Serena Vettori;Gabriela Riemekasten;Elise Siegert;Simona Rednic;Ileana Nicoara;André Kahan;P. Vlachoyiannopoulos;C. Montecucco;Roberto Caporali;Patricia E. Carreira;Srdan Novak;László Czirják;Cecilia Varju;Carlo Chizzolini;Eugene J. Kucharz;Anna Kotulska;Magdalena Kopec Medrek;Malgorzata Widuchowska;Franco Cozzi;Blaz Rozman;Carmel Mallia;Bernard Coleiro;Armando Gabrielli;Dominique Farge;Chen Wu;Zora Marjanovic;Helene Faivre;Darin Hij;Roza Dhamadi;Paolo Airò;Roger Hesselstrand;Frank Wollheim;Dirk M. Wuttge;Kristofer Andréasson;Duska Martinovic;Alexandra Balbir Gurman;Yolanda Braun Moscovici;F. Trotta;Andrea Lo Monaco;Nicolas Hunzelmann;Raffaele Pellerito;Ospedale Mauriziano;Lisa Maria Bambara;Paola Caramaschi;Carol Black;Christopher Denton;Nemanja Damjanov;Jörg Henes;Vera Ortiz Santamaria;Stefan Heitmann;Dorota Krasowska;Matthias Seidel;Harald Burkhardt;Andrea Himsel;Maria J. Salvador;José Antonio Pereira Da Silva;Bojana Stamenkovic;Aleksandra Stankovic;Niska Banja;Mohammed Tikly;Lidia P. Ananieva;Lev N. Denisov;Ulf Müller Ladner;Marc Frerix;Ingo Tarner;Raffaella Scorza;Merete Engelhart;Gitte Strauss;Henrik Nielsen;Kirsten Damgaard;Antonio Zea Mendoza;Carlos de la Puente;Walter A. Sifuentes Giraldo;Øyvind Midtvedt;Silje Reiseter;Eric Hachulla;David Launay;Guido Valesini;Valeria Riccieri;Ruxandra Maria Ionescu;Daniela Opris;Laura Groseanu;Roxana Sfrent Cornateanu;Razvan Ionitescu;Ana Maria Gherghe;Alina Soare;Marilena Gorga;Mihai Bojinca;Georg Schett;Jörg HW Distler;Christian Beyer;Pierluigi Meroni;Francesca Ingegnoli;Luc Mouthon;Filip De Keyser;Vanessa Smith;Francesco P. Cantatore;Ada Corrado;Maria R. Pozzi;Kilian Eyerich;Rüdiger Hein;Elisabeth Knott;Piotr Wiland;Magdalena Szmyrka Kaczmarek;Renata Sokolik;Ewa Morgiel;Marta Madej;Brigitte Krummel Lorenz;Martin Aringer;Claudia Günther;Rene Westhovens;Ellen de Langhe;Jan Lenaerts;Branimir Anic;Marko Baresic;Miroslav Mayer;Sebastião C. Radominski;Carolina de Souza Müller;Valderílio F. Azevedo;Svetlana Agachi;Liliana Groppa;Lealea Chiaburu;Eugen Russu;Sergei Popa;Thierry Zenone;Simon Stebbings;John Highton;Lisa Stamp;Peter Chapman;John O’Donnell;Kamal Solanki;Alan Doube;Douglas Veale;Marie O'Rourke;Esthela Loyo;Mengtao Li;Edoardo Rosato;Antonio Amoroso;Antonietta Gigante;Cristina Mihaela Tanaseanu;Monica Popescu;Alina Dumitrascu;Isabela Tiglea;Rosario Foti;Rodica Chirieac;Codrina Ancuta;Peter Villiger;Sabine Adler;Paloma García de la Peña Lefebvre;Silvia Rodriguez Rubio;Marta Valero Exposito;Jean Sibilia;Emmanuel Chatelus;Jacques Eric Gottenberg;Hélène Chifflot;Ira Litinsky;Algirdas Venalis;Irena Butrimiene;Paulius Venalis;Rita Rugiene;Diana Karpec;Lesley Ann Saketkoo;Joseph A. Lasky;Eduardo Kerzberg;Fabiana Montoya;Vanesa Cosentino;Massimiliano Limonta;Antonio Luca Brucato;Elide Lupi;François Spertini;Camillo Ribi;Guillaume Buss;Jean Louis Pasquali;Thierry Martin;Audrey Gorse
2016-01-01
Abstract
Abstract
OBJECTIVES:
Improvement of skin fibrosis is part of the natural course of diffuse cutaneous systemic sclerosis (dcSSc). Recognising those patients most likely to improve could help tailoring clinical management and cohort enrichment for clinical trials. In this study, we aimed to identify predictors for improvement of skin fibrosis in patients with dcSSc.
METHODS:
We performed a longitudinal analysis of the European Scleroderma Trials And Research (EUSTAR) registry including patients with dcSSc, fulfilling American College of Rheumatology criteria, baseline modified Rodnan skin score (mRSS) ≥7 and follow-up mRSS at 12±2 months. The primary outcome was skin improvement (decrease in mRSS of >5 points and ≥25%) at 1 year follow-up. A respective increase in mRSS was considered progression. Candidate predictors for skin improvement were selected by expert opinion and logistic regression with bootstrap validation was applied.
RESULTS:
From the 919 patients included, 218 (24%) improved and 95 (10%) progressed. Eleven candidate predictors for skin improvement were analysed. The final model identified high baseline mRSS and absence of tendon friction rubs as independent predictors of skin improvement. The baseline mRSS was the strongest predictor of skin improvement, independent of disease duration. An upper threshold between 18 and 25 performed best in enriching for progressors over regressors.
CONCLUSIONS:
Patients with advanced skin fibrosis at baseline and absence of tendon friction rubs are more likely to regress in the next year than patients with milder skin fibrosis. These evidence-based data can be implemented in clinical trial design to minimise the inclusion of patients who would regress under standard of care.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/154379
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.